Distribution of Scores: Population-level Interpretation Distribution of Scores: Clinical-level Interpretation Neuropsychological Testing Results Behavioral Ratings Results ## load libraries library(bwu) library(crosstalk) library(gifski) library(highcharter) ## Registered S3 method overwritten by 'quantmod': ## method from ## as.zoo.data.frame zoo ## Highcharts (www.highcharts.com) is a Highsoft software product which is ## not free for commercial and Governmental use library(htmlwidgets) library(knitr) library(languageserver) library(manipulateWidget) library(reactable) library(revealjs) library(rmarkdown) ## ## Attaching package: 'rmarkdown' ## The following object is masked from 'package:languageserver': ## ## run ## The following object is masked from 'package:bwu': ## ## tufte_handout library(svglite) library(tibble) library(tidyverse) ## ── Attaching packages ## ────────────────────────────────── tidyverse ## 1.

Where does the summary show up?

Inputs and Outputs Embedded Application This R Markdown document is made interactive using Shiny. Unlike the more traditional workflow of creating static reports, you can now create documents that allow your readers to change the assumptions underlying your analysis and see the results immediately.
To learn more, see Interactive Documents.
Inputs and Outputs You can embed Shiny inputs and outputs in your document.

Drilldown plot in highcharter trying to post

R Markdown Including Plots R Markdown This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see http://rmarkdown.rstudio.com.
You can embed an R code chunk like this:
summary(cars) ## speed dist ## Min. : 4.0 Min. : 2.00 ## 1st Qu.:12.0 1st Qu.: 26.00 ## Median :15.0 Median : 36.

For a demonstration of a line plot on a polar axis, see @fig-polar.

```
import numpy as np
import matplotlib.pyplot as plt
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
fig, ax = plt.subplots(subplot_kw={'projection': 'polar'})
ax.plot(theta, r)
ax.set_rticks([0.5, 1, 1.5, 2])
ax.grid(True)
plt.show()
```